We conclude by showcasing the usefulness of the landing algorithm on an array of optimization problems.
The deep learning experiments are run on a single Tesla V100 GPU with Pytorch [1]}, while the other experiments are run on a small laptop CPU and Numpy [2]}.
The code for the landing flow as a Pytorch Optimizer and to reproduce the experiments is available at https://github.com/pierreablin/landing.
In all experiments, we use the safe rule for the step-size described in prop:saferule, with \(\varepsilon =0.5\) , and set \(\lambda =1\) .
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